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Novel eigenvector centrality indices for octane isomers to explore their physicochemical properties
A Salini Jancy Rani1, B J Balamurugan2
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, Vandalur-Kelambakkam Road, Chennai, 600127, Tamil Nadu, India.
Scientific Reports
|October 6, 2025
Summary
New eigenvector centrality topological indices accurately predict octane isomer properties. These cheminformatics tools offer strong correlations for Quantitative Structure-Property Relationship (QSPR) analysis and predictive modeling.
Area of Science:
- Chemical graph theory
- Computational chemistry
- Cheminformatics
Background:
- Molecular structures are represented as graphs, with atoms as vertices and bonds as edges.
- Centrality measures are crucial for identifying influential atoms within molecular graphs.
- Eigenvector centrality quantifies atom importance based on network connections.
Purpose of the Study:
- Introduce seven novel eigenvector centrality-based topological indices.
- Apply these indices to octane isomers for QSPR analysis.
- Investigate correlations between indices and molecular properties.
Main Methods:
- Calculated eigenvector centrality for atoms in octane isomers.
- Developed topological indices based on eigenvector centrality.
- Performed QSPR analysis using these indices to predict properties like density and entropy.
- Validated regression models using Y-randomization and chi-square tests.
Main Results:
- Established statistically significant and strong correlations between the new indices and octane isomer properties.
- Demonstrated the predictive power of eigenvector centrality-based topological indices.
- Confirmed the reliability and accuracy of the developed regression models.
Conclusions:
- The novel eigenvector centrality-based topological indices are effective for QSPR analysis.
- These indices show significant potential for cheminformatics-based predictive modeling.
- The study validates the utility of graph-theoretical centrality measures in predicting chemical properties.
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